<<<<<<< Updated upstream Pandas Profiling Report

Overview

Dataset statistics

Number of variables28
Number of observations15344
Missing cells15342
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 MiB
Average record size in memory224.0 B

Variable types

Categorical11
DateTime1
TimeSeries15
Numeric1

Alerts

State Code has constant value "4" Constant
County Code has constant value "13" Constant
Site Num has constant value "9997" Constant
Address has constant value "4530 N 17TH AVENUE" Constant
State has constant value "Arizona" Constant
County has constant value "Maricopa" Constant
City has constant value "Phoenix" Constant
NO2 Units has constant value "Parts per billion" Constant
O3 Units has constant value "Parts per million" Constant
SO2 Units has constant value "Parts per billion" Constant
CO Units has constant value "Parts per million" Constant
CO 1st Max Hour is highly correlated with NO2 1st Max HourHigh correlation
State Code is highly correlated with Site Num and 9 other fieldsHigh correlation
County Code is highly correlated with State Code and 9 other fieldsHigh correlation
Site Num is highly correlated with State Code and 9 other fieldsHigh correlation
Address is highly correlated with State Code and 9 other fieldsHigh correlation
State is highly correlated with State Code and 9 other fieldsHigh correlation
County is highly correlated with State Code and 9 other fieldsHigh correlation
City is highly correlated with State Code and 9 other fieldsHigh correlation
NO2 Units is highly correlated with State Code and 9 other fieldsHigh correlation
O3 Units is highly correlated with State Code and 9 other fieldsHigh correlation
SO2 Units is highly correlated with State Code and 9 other fieldsHigh correlation
CO Units is highly correlated with State Code and 9 other fieldsHigh correlation
NO2 Mean is highly correlated with NO2 1st Max Value and 5 other fieldsHigh correlation
NO2 1st Max Value is highly correlated with NO2 Mean and 2 other fieldsHigh correlation
NO2 AQI is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
O3 Mean is highly correlated with NO2 Mean and 3 other fieldsHigh correlation
O3 1st Max Value is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
O3 AQI is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
SO2 Mean is highly correlated with SO2 1st Max Value and 1 other fieldsHigh correlation
SO2 1st Max Value is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
SO2 AQI is highly correlated with NO2 AQI and 4 other fieldsHigh correlation
CO Mean is highly correlated with NO2 Mean and 2 other fieldsHigh correlation
CO 1st Max Value is highly correlated with NO2 Mean and 4 other fieldsHigh correlation
CO AQI is highly correlated with NO2 1st Max Hour and 5 other fieldsHigh correlation
NO2 1st Max Hour is highly correlated with NO2 AQI and 6 other fieldsHigh correlation
O3 1st Max Hour is highly correlated with NO2 1st Max Hour and 3 other fieldsHigh correlation
SO2 1st Max Hour is highly correlated with SO2 1st Max ValueHigh correlation
SO2 AQI has 7672 (50.0%) missing values Missing
CO AQI has 7670 (50.0%) missing values Missing
NO2 Mean is non stationary Non stationary
NO2 1st Max Value is non stationary Non stationary
NO2 AQI is non stationary Non stationary
O3 Mean is non stationary Non stationary
O3 1st Max Value is non stationary Non stationary
O3 AQI is non stationary Non stationary
SO2 Mean is non stationary Non stationary
SO2 1st Max Value is non stationary Non stationary
SO2 1st Max Hour is non stationary Non stationary
SO2 AQI is non stationary Non stationary
CO Mean is non stationary Non stationary
CO 1st Max Value is non stationary Non stationary
CO AQI is non stationary Non stationary
NO2 Mean is seasonal Seasonal
NO2 1st Max Value is seasonal Seasonal
NO2 AQI is seasonal Seasonal
O3 Mean is seasonal Seasonal
O3 1st Max Value is seasonal Seasonal
O3 AQI is seasonal Seasonal
SO2 Mean is seasonal Seasonal
SO2 1st Max Value is seasonal Seasonal
SO2 1st Max Hour is seasonal Seasonal
SO2 AQI is seasonal Seasonal
CO Mean is seasonal Seasonal
CO 1st Max Value is seasonal Seasonal
CO AQI is seasonal Seasonal
NO2 1st Max Hour has 1930 (12.6%) zeros Zeros
SO2 Mean has 204 (1.3%) zeros Zeros
SO2 1st Max Value has 204 (1.3%) zeros Zeros
SO2 1st Max Hour has 2418 (15.8%) zeros Zeros
SO2 AQI has 520 (3.4%) zeros Zeros
CO 1st Max Hour has 2898 (18.9%) zeros Zeros

Reproduction

Analysis started2022-10-20 17:53:17.346644
Analysis finished2022-10-20 17:53:35.682202
Duration18.34 seconds
Software versionpandas-profiling v3.3.1
Download configurationconfig.json

Variables

State Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
4
15344 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15344
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
415344
100.0%

Length

2022-10-20T18:53:35.776034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Overview

Dataset statistics

Number of variables28
Number of observations15344
Missing cells15342
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 MiB
Average record size in memory224.0 B

Variable types

Categorical11
DateTime1
TimeSeries15
Numeric1

Alerts

State Code has constant value "4" Constant
County Code has constant value "13" Constant
Site Num has constant value "9997" Constant
Address has constant value "4530 N 17TH AVENUE" Constant
State has constant value "Arizona" Constant
County has constant value "Maricopa" Constant
City has constant value "Phoenix" Constant
NO2 Units has constant value "Parts per billion" Constant
O3 Units has constant value "Parts per million" Constant
SO2 Units has constant value "Parts per billion" Constant
CO Units has constant value "Parts per million" Constant
CO 1st Max Hour is highly correlated with NO2 1st Max HourHigh correlation
State Code is highly correlated with Address and 9 other fieldsHigh correlation
County Code is highly correlated with Address and 9 other fieldsHigh correlation
Site Num is highly correlated with Address and 9 other fieldsHigh correlation
Address is highly correlated with State and 9 other fieldsHigh correlation
State is highly correlated with Address and 9 other fieldsHigh correlation
County is highly correlated with Address and 9 other fieldsHigh correlation
City is highly correlated with Address and 9 other fieldsHigh correlation
NO2 Units is highly correlated with Address and 9 other fieldsHigh correlation
O3 Units is highly correlated with Address and 9 other fieldsHigh correlation
SO2 Units is highly correlated with Address and 9 other fieldsHigh correlation
CO Units is highly correlated with Address and 9 other fieldsHigh correlation
NO2 Mean is highly correlated with NO2 1st Max Value and 5 other fieldsHigh correlation
NO2 1st Max Value is highly correlated with NO2 Mean and 2 other fieldsHigh correlation
NO2 AQI is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
O3 Mean is highly correlated with NO2 Mean and 3 other fieldsHigh correlation
O3 1st Max Value is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
O3 AQI is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
SO2 Mean is highly correlated with SO2 1st Max Value and 1 other fieldsHigh correlation
SO2 1st Max Value is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
SO2 AQI is highly correlated with NO2 AQI and 4 other fieldsHigh correlation
CO Mean is highly correlated with NO2 Mean and 2 other fieldsHigh correlation
CO 1st Max Value is highly correlated with NO2 Mean and 4 other fieldsHigh correlation
CO AQI is highly correlated with NO2 1st Max Hour and 5 other fieldsHigh correlation
NO2 1st Max Hour is highly correlated with NO2 AQI and 6 other fieldsHigh correlation
O3 1st Max Hour is highly correlated with NO2 1st Max Hour and 3 other fieldsHigh correlation
SO2 1st Max Hour is highly correlated with SO2 1st Max ValueHigh correlation
SO2 AQI has 7672 (50.0%) missing values Missing
CO AQI has 7670 (50.0%) missing values Missing
NO2 Mean is non stationary Non stationary
NO2 1st Max Value is non stationary Non stationary
NO2 AQI is non stationary Non stationary
O3 Mean is non stationary Non stationary
O3 1st Max Value is non stationary Non stationary
O3 AQI is non stationary Non stationary
SO2 Mean is non stationary Non stationary
SO2 1st Max Value is non stationary Non stationary
SO2 1st Max Hour is non stationary Non stationary
SO2 AQI is non stationary Non stationary
CO Mean is non stationary Non stationary
CO 1st Max Value is non stationary Non stationary
CO AQI is non stationary Non stationary
NO2 Mean is seasonal Seasonal
NO2 1st Max Value is seasonal Seasonal
NO2 AQI is seasonal Seasonal
O3 Mean is seasonal Seasonal
O3 1st Max Value is seasonal Seasonal
O3 AQI is seasonal Seasonal
SO2 Mean is seasonal Seasonal
SO2 1st Max Value is seasonal Seasonal
SO2 1st Max Hour is seasonal Seasonal
SO2 AQI is seasonal Seasonal
CO Mean is seasonal Seasonal
CO 1st Max Value is seasonal Seasonal
CO AQI is seasonal Seasonal
NO2 1st Max Hour has 1930 (12.6%) zeros Zeros
SO2 Mean has 204 (1.3%) zeros Zeros
SO2 1st Max Value has 204 (1.3%) zeros Zeros
SO2 1st Max Hour has 2418 (15.8%) zeros Zeros
SO2 AQI has 520 (3.4%) zeros Zeros
CO 1st Max Hour has 2898 (18.9%) zeros Zeros

Reproduction

Analysis started2022-10-20 18:31:12.081723
Analysis finished2022-10-20 18:31:24.774539
Duration12.69 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

State Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
4
15344 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15344
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
415344
100.0%

Length

2022-10-20T19:31:24.822397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:53:35.913536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:24.895401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
415344
100.0%

Most occurring characters

ValueCountFrequency (%)
415344
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15344
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
415344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15344
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
415344
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
415344
100.0%

County Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
13
15344 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters30688
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row13
3rd row13
4th row13
5th row13

Common Values

ValueCountFrequency (%)
1315344
100.0%

Length

2022-10-20T18:53:36.027423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
415344
100.0%

Most occurring characters

ValueCountFrequency (%)
415344
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15344
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
415344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15344
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
415344
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
415344
100.0%

County Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
13
15344 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters30688
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row13
3rd row13
4th row13
5th row13

Common Values

ValueCountFrequency (%)
1315344
100.0%

Length

2022-10-20T19:31:24.958004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:53:36.168505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:25.030110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1315344
100.0%

Most occurring characters

ValueCountFrequency (%)
115344
50.0%
315344
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30688
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
115344
50.0%
315344
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common30688
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
115344
50.0%
315344
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
115344
50.0%
315344
50.0%

Site Num
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
9997
15344 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters61376
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9997
2nd row9997
3rd row9997
4th row9997
5th row9997

Common Values

ValueCountFrequency (%)
999715344
100.0%

Length

2022-10-20T18:53:36.290513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1315344
100.0%

Most occurring characters

ValueCountFrequency (%)
115344
50.0%
315344
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30688
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
115344
50.0%
315344
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common30688
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
115344
50.0%
315344
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
115344
50.0%
315344
50.0%

Site Num
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
9997
15344 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters61376
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9997
2nd row9997
3rd row9997
4th row9997
5th row9997

Common Values

ValueCountFrequency (%)
999715344
100.0%

Length

2022-10-20T19:31:25.094506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:53:36.442144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:25.179303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
999715344
100.0%

Most occurring characters

ValueCountFrequency (%)
946032
75.0%
715344
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number61376
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
946032
75.0%
715344
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common61376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
946032
75.0%
715344
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII61376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
946032
75.0%
715344
 
25.0%

Address
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
4530 N 17TH AVENUE
15344 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters276192
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4530 N 17TH AVENUE
2nd row4530 N 17TH AVENUE
3rd row4530 N 17TH AVENUE
4th row4530 N 17TH AVENUE
5th row4530 N 17TH AVENUE

Common Values

ValueCountFrequency (%)
4530 N 17TH AVENUE15344
100.0%

Length

2022-10-20T18:53:36.583584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
999715344
100.0%

Most occurring characters

ValueCountFrequency (%)
946032
75.0%
715344
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number61376
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
946032
75.0%
715344
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common61376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
946032
75.0%
715344
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII61376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
946032
75.0%
715344
 
25.0%

Address
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
4530 N 17TH AVENUE
15344 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters276192
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4530 N 17TH AVENUE
2nd row4530 N 17TH AVENUE
3rd row4530 N 17TH AVENUE
4th row4530 N 17TH AVENUE
5th row4530 N 17TH AVENUE

Common Values

ValueCountFrequency (%)
4530 N 17TH AVENUE15344
100.0%

Length

2022-10-20T19:31:25.242112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:53:36.732046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:25.315157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
453015344
25.0%
n15344
25.0%
17th15344
25.0%
avenue15344
25.0%

Most occurring characters

ValueCountFrequency (%)
46032
16.7%
N30688
11.1%
E30688
11.1%
415344
 
5.6%
515344
 
5.6%
315344
 
5.6%
015344
 
5.6%
115344
 
5.6%
715344
 
5.6%
T15344
 
5.6%
Other values (4)61376
22.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter138096
50.0%
Decimal Number92064
33.3%
Space Separator46032
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N30688
22.2%
E30688
22.2%
T15344
11.1%
H15344
11.1%
A15344
11.1%
V15344
11.1%
U15344
11.1%
Decimal Number
ValueCountFrequency (%)
415344
16.7%
515344
16.7%
315344
16.7%
015344
16.7%
115344
16.7%
715344
16.7%
Space Separator
ValueCountFrequency (%)
46032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common138096
50.0%
Latin138096
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
46032
33.3%
415344
 
11.1%
515344
 
11.1%
315344
 
11.1%
015344
 
11.1%
115344
 
11.1%
715344
 
11.1%
Latin
ValueCountFrequency (%)
N30688
22.2%
E30688
22.2%
T15344
11.1%
H15344
11.1%
A15344
11.1%
V15344
11.1%
U15344
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII276192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46032
16.7%
N30688
11.1%
E30688
11.1%
415344
 
5.6%
515344
 
5.6%
315344
 
5.6%
015344
 
5.6%
115344
 
5.6%
715344
 
5.6%
T15344
 
5.6%
Other values (4)61376
22.2%

State
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Arizona
15344 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters107408
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArizona
2nd rowArizona
3rd rowArizona
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
Arizona15344
100.0%

Length

2022-10-20T18:53:36.866068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
453015344
25.0%
n15344
25.0%
17th15344
25.0%
avenue15344
25.0%

Most occurring characters

ValueCountFrequency (%)
46032
16.7%
N30688
11.1%
E30688
11.1%
415344
 
5.6%
515344
 
5.6%
315344
 
5.6%
015344
 
5.6%
115344
 
5.6%
715344
 
5.6%
T15344
 
5.6%
Other values (4)61376
22.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter138096
50.0%
Decimal Number92064
33.3%
Space Separator46032
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N30688
22.2%
E30688
22.2%
T15344
11.1%
H15344
11.1%
A15344
11.1%
V15344
11.1%
U15344
11.1%
Decimal Number
ValueCountFrequency (%)
415344
16.7%
515344
16.7%
315344
16.7%
015344
16.7%
115344
16.7%
715344
16.7%
Space Separator
ValueCountFrequency (%)
46032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common138096
50.0%
Latin138096
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
46032
33.3%
415344
 
11.1%
515344
 
11.1%
315344
 
11.1%
015344
 
11.1%
115344
 
11.1%
715344
 
11.1%
Latin
ValueCountFrequency (%)
N30688
22.2%
E30688
22.2%
T15344
11.1%
H15344
11.1%
A15344
11.1%
V15344
11.1%
U15344
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII276192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46032
16.7%
N30688
11.1%
E30688
11.1%
415344
 
5.6%
515344
 
5.6%
315344
 
5.6%
015344
 
5.6%
115344
 
5.6%
715344
 
5.6%
T15344
 
5.6%
Other values (4)61376
22.2%

State
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Arizona
15344 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters107408
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArizona
2nd rowArizona
3rd rowArizona
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
Arizona15344
100.0%

Length

2022-10-20T19:31:25.377531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:53:37.002930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:25.450726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
arizona15344
100.0%

Most occurring characters

ValueCountFrequency (%)
A15344
14.3%
r15344
14.3%
i15344
14.3%
z15344
14.3%
o15344
14.3%
n15344
14.3%
a15344
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter92064
85.7%
Uppercase Letter15344
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r15344
16.7%
i15344
16.7%
z15344
16.7%
o15344
16.7%
n15344
16.7%
a15344
16.7%
Uppercase Letter
ValueCountFrequency (%)
A15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin107408
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A15344
14.3%
r15344
14.3%
i15344
14.3%
z15344
14.3%
o15344
14.3%
n15344
14.3%
a15344
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII107408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A15344
14.3%
r15344
14.3%
i15344
14.3%
z15344
14.3%
o15344
14.3%
n15344
14.3%
a15344
14.3%

County
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Maricopa
15344 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters122752
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaricopa
2nd rowMaricopa
3rd rowMaricopa
4th rowMaricopa
5th rowMaricopa

Common Values

ValueCountFrequency (%)
Maricopa15344
100.0%

Length

2022-10-20T18:53:37.124144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
arizona15344
100.0%

Most occurring characters

ValueCountFrequency (%)
A15344
14.3%
r15344
14.3%
i15344
14.3%
z15344
14.3%
o15344
14.3%
n15344
14.3%
a15344
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter92064
85.7%
Uppercase Letter15344
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r15344
16.7%
i15344
16.7%
z15344
16.7%
o15344
16.7%
n15344
16.7%
a15344
16.7%
Uppercase Letter
ValueCountFrequency (%)
A15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin107408
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A15344
14.3%
r15344
14.3%
i15344
14.3%
z15344
14.3%
o15344
14.3%
n15344
14.3%
a15344
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII107408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A15344
14.3%
r15344
14.3%
i15344
14.3%
z15344
14.3%
o15344
14.3%
n15344
14.3%
a15344
14.3%

County
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Maricopa
15344 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters122752
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaricopa
2nd rowMaricopa
3rd rowMaricopa
4th rowMaricopa
5th rowMaricopa

Common Values

ValueCountFrequency (%)
Maricopa15344
100.0%

Length

2022-10-20T19:31:25.512818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:53:37.279617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:25.585208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
maricopa15344
100.0%

Most occurring characters

ValueCountFrequency (%)
a30688
25.0%
M15344
12.5%
r15344
12.5%
i15344
12.5%
c15344
12.5%
o15344
12.5%
p15344
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter107408
87.5%
Uppercase Letter15344
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a30688
28.6%
r15344
14.3%
i15344
14.3%
c15344
14.3%
o15344
14.3%
p15344
14.3%
Uppercase Letter
ValueCountFrequency (%)
M15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin122752
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a30688
25.0%
M15344
12.5%
r15344
12.5%
i15344
12.5%
c15344
12.5%
o15344
12.5%
p15344
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII122752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a30688
25.0%
M15344
12.5%
r15344
12.5%
i15344
12.5%
c15344
12.5%
o15344
12.5%
p15344
12.5%

City
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Phoenix
15344 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters107408
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhoenix
2nd rowPhoenix
3rd rowPhoenix
4th rowPhoenix
5th rowPhoenix

Common Values

ValueCountFrequency (%)
Phoenix15344
100.0%

Length

2022-10-20T18:53:37.418795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
maricopa15344
100.0%

Most occurring characters

ValueCountFrequency (%)
a30688
25.0%
M15344
12.5%
r15344
12.5%
i15344
12.5%
c15344
12.5%
o15344
12.5%
p15344
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter107408
87.5%
Uppercase Letter15344
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a30688
28.6%
r15344
14.3%
i15344
14.3%
c15344
14.3%
o15344
14.3%
p15344
14.3%
Uppercase Letter
ValueCountFrequency (%)
M15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin122752
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a30688
25.0%
M15344
12.5%
r15344
12.5%
i15344
12.5%
c15344
12.5%
o15344
12.5%
p15344
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII122752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a30688
25.0%
M15344
12.5%
r15344
12.5%
i15344
12.5%
c15344
12.5%
o15344
12.5%
p15344
12.5%

City
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Phoenix
15344 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters107408
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhoenix
2nd rowPhoenix
3rd rowPhoenix
4th rowPhoenix
5th rowPhoenix

Common Values

ValueCountFrequency (%)
Phoenix15344
100.0%

Length

2022-10-20T19:31:25.648753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:53:37.585554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:25.724973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
phoenix15344
100.0%

Most occurring characters

ValueCountFrequency (%)
P15344
14.3%
h15344
14.3%
o15344
14.3%
e15344
14.3%
n15344
14.3%
i15344
14.3%
x15344
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter92064
85.7%
Uppercase Letter15344
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h15344
16.7%
o15344
16.7%
e15344
16.7%
n15344
16.7%
i15344
16.7%
x15344
16.7%
Uppercase Letter
ValueCountFrequency (%)
P15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin107408
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P15344
14.3%
h15344
14.3%
o15344
14.3%
e15344
14.3%
n15344
14.3%
i15344
14.3%
x15344
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII107408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P15344
14.3%
h15344
14.3%
o15344
14.3%
e15344
14.3%
n15344
14.3%
i15344
14.3%
x15344
14.3%
Distinct3409
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Minimum2005-03-04 00:00:00
Maximum2016-03-26 00:00:00
2022-10-20T18:53:37.755473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
phoenix15344
100.0%

Most occurring characters

ValueCountFrequency (%)
P15344
14.3%
h15344
14.3%
o15344
14.3%
e15344
14.3%
n15344
14.3%
i15344
14.3%
x15344
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter92064
85.7%
Uppercase Letter15344
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h15344
16.7%
o15344
16.7%
e15344
16.7%
n15344
16.7%
i15344
16.7%
x15344
16.7%
Uppercase Letter
ValueCountFrequency (%)
P15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin107408
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P15344
14.3%
h15344
14.3%
o15344
14.3%
e15344
14.3%
n15344
14.3%
i15344
14.3%
x15344
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII107408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P15344
14.3%
h15344
14.3%
o15344
14.3%
e15344
14.3%
n15344
14.3%
i15344
14.3%
x15344
14.3%
Distinct3409
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Minimum2005-03-04 00:00:00
Maximum2016-03-26 00:00:00
2022-10-20T19:31:25.800411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:53:37.948332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:31:25.899547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

NO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Parts per billion
15344 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters260848
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion15344
100.0%

Length

2022-10-20T18:53:38.129273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

NO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Parts per billion
15344 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters260848
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion15344
100.0%

Length

2022-10-20T19:31:25.992827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:53:38.267703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:26.069869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts15344
33.3%
per15344
33.3%
billion15344
33.3%

Most occurring characters

ValueCountFrequency (%)
r30688
11.8%
30688
11.8%
i30688
11.8%
l30688
11.8%
P15344
 
5.9%
a15344
 
5.9%
t15344
 
5.9%
s15344
 
5.9%
p15344
 
5.9%
e15344
 
5.9%
Other values (3)46032
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter214816
82.4%
Space Separator30688
 
11.8%
Uppercase Letter15344
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r30688
14.3%
i30688
14.3%
l30688
14.3%
a15344
7.1%
t15344
7.1%
s15344
7.1%
p15344
7.1%
e15344
7.1%
b15344
7.1%
o15344
7.1%
Space Separator
ValueCountFrequency (%)
30688
100.0%
Uppercase Letter
ValueCountFrequency (%)
P15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin230160
88.2%
Common30688
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r30688
13.3%
i30688
13.3%
l30688
13.3%
P15344
6.7%
a15344
6.7%
t15344
6.7%
s15344
6.7%
p15344
6.7%
e15344
6.7%
b15344
6.7%
Other values (2)30688
13.3%
Common
ValueCountFrequency (%)
30688
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII260848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r30688
11.8%
30688
11.8%
i30688
11.8%
l30688
11.8%
P15344
 
5.9%
a15344
 
5.9%
t15344
 
5.9%
s15344
 
5.9%
p15344
 
5.9%
e15344
 
5.9%
Other values (3)46032
17.6%

NO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1486
Distinct (%)0.09684567257559959
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean18.76470994577685
Minimum0.0
Maximum55.208333
Zeros110
Zeros (%)0.007168925964546402
Memory size122880
2022-10-20T18:53:38.376607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts15344
33.3%
per15344
33.3%
billion15344
33.3%

Most occurring characters

ValueCountFrequency (%)
r30688
11.8%
30688
11.8%
i30688
11.8%
l30688
11.8%
P15344
 
5.9%
a15344
 
5.9%
t15344
 
5.9%
s15344
 
5.9%
p15344
 
5.9%
e15344
 
5.9%
Other values (3)46032
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter214816
82.4%
Space Separator30688
 
11.8%
Uppercase Letter15344
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r30688
14.3%
i30688
14.3%
l30688
14.3%
a15344
7.1%
t15344
7.1%
s15344
7.1%
p15344
7.1%
e15344
7.1%
b15344
7.1%
o15344
7.1%
Space Separator
ValueCountFrequency (%)
30688
100.0%
Uppercase Letter
ValueCountFrequency (%)
P15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin230160
88.2%
Common30688
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r30688
13.3%
i30688
13.3%
l30688
13.3%
P15344
6.7%
a15344
6.7%
t15344
6.7%
s15344
6.7%
p15344
6.7%
e15344
6.7%
b15344
6.7%
Other values (2)30688
13.3%
Common
ValueCountFrequency (%)
30688
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII260848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r30688
11.8%
30688
11.8%
i30688
11.8%
l30688
11.8%
P15344
 
5.9%
a15344
 
5.9%
t15344
 
5.9%
s15344
 
5.9%
p15344
 
5.9%
e15344
 
5.9%
Other values (3)46032
17.6%

NO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1486
Distinct (%)0.09684567257559959
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean18.76470994577685
Minimum0.0
Maximum55.208333
Zeros110
Zeros (%)0.007168925964546402
Memory size122880
2022-10-20T19:31:26.129882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.83958305
Q111.74270825
median18.375
Q325.25
95-th percentile32.87437495
Maximum55.208333
Range55.208333
Interquartile range (IQR)13.50729175

Descriptive statistics

Standard deviation8.604452653
Coefficient of variation (CV)0.4585443994
Kurtosis-0.5552903222
Mean18.76470995
Median Absolute Deviation (MAD)6.76875
Skewness0.2291276811
Sum287925.7094
Variance74.03660545
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.273069146 × 10-10
2022-10-20T18:53:38.556065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.83958305
Q111.74270825
median18.375
Q325.25
95-th percentile32.87437495
Maximum55.208333
Range55.208333
Interquartile range (IQR)13.50729175

Descriptive statistics

Standard deviation8.604452653
Coefficient of variation (CV)0.4585443994
Kurtosis-0.5552903222
Mean18.76470995
Median Absolute Deviation (MAD)6.76875
Skewness0.2291276811
Sum287925.7094
Variance74.03660545
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.273069146 × 10-10
2022-10-20T19:31:26.221477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0110
 
0.7%
20.552
 
0.3%
24.58333348
 
0.3%
1748
 
0.3%
23.548
 
0.3%
15.45833344
 
0.3%
17.91666744
 
0.3%
9.2544
 
0.3%
15.79166744
 
0.3%
10.2540
 
0.3%
Other values (1476)14822
96.6%
ValueCountFrequency (%)
0110
0.7%
2.1666674
 
< 0.1%
2.3333334
 
< 0.1%
2.754
 
< 0.1%
2.8333338
 
0.1%
2.8754
 
< 0.1%
2.9583334
 
< 0.1%
38
 
0.1%
3.0833334
 
< 0.1%
3.1254
 
< 0.1%
ValueCountFrequency (%)
55.2083334
< 0.1%
48.0833334
< 0.1%
46.9166674
< 0.1%
45.5416674
< 0.1%
45.3754
< 0.1%
45.1818184
< 0.1%
44.6666674
< 0.1%
44.4583334
< 0.1%
43.3754
< 0.1%
43.2083334
< 0.1%
2022-10-20T18:53:38.980688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0110
 
0.7%
20.552
 
0.3%
24.58333348
 
0.3%
1748
 
0.3%
23.548
 
0.3%
15.45833344
 
0.3%
17.91666744
 
0.3%
9.2544
 
0.3%
15.79166744
 
0.3%
10.2540
 
0.3%
Other values (1476)14822
96.6%
ValueCountFrequency (%)
0110
0.7%
2.1666674
 
< 0.1%
2.3333334
 
< 0.1%
2.754
 
< 0.1%
2.8333338
 
0.1%
2.8754
 
< 0.1%
2.9583334
 
< 0.1%
38
 
0.1%
3.0833334
 
< 0.1%
3.1254
 
< 0.1%
ValueCountFrequency (%)
55.2083334
< 0.1%
48.0833334
< 0.1%
46.9166674
< 0.1%
45.5416674
< 0.1%
45.3754
< 0.1%
45.1818184
< 0.1%
44.6666674
< 0.1%
44.4583334
< 0.1%
43.3754
< 0.1%
43.2083334
< 0.1%
2022-10-20T19:31:26.406764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct349
Distinct (%)0.02274504692387904
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean38.199113660062565
Minimum0.0
Maximum89.0
Zeros110
Zeros (%)0.007168925964546402
Memory size122880
2022-10-20T18:53:39.229347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct349
Distinct (%)0.02274504692387904
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean38.199113660062565
Minimum0.0
Maximum89.0
Zeros110
Zeros (%)0.007168925964546402
Memory size122880
2022-10-20T19:31:26.542334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q130
median40
Q347
95-th percentile57
Maximum89
Range89
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.04141341
Coefficient of variation (CV)0.3414061783
Kurtosis-0.01916063254
Mean38.19911366
Median Absolute Deviation (MAD)8
Skewness-0.3811381579
Sum586127.2
Variance170.0784637
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.547506278 × 10-14
2022-10-20T18:53:39.398571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q130
median40
Q347
95-th percentile57
Maximum89
Range89
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.04141341
Coefficient of variation (CV)0.3414061783
Kurtosis-0.01916063254
Mean38.19911366
Median Absolute Deviation (MAD)8
Skewness-0.3811381579
Sum586127.2
Variance170.0784637
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.547506278 × 10-14
2022-10-20T19:31:26.638151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41508
 
3.3%
40508
 
3.3%
44504
 
3.3%
45496
 
3.2%
42476
 
3.1%
47468
 
3.1%
43460
 
3.0%
46432
 
2.8%
38424
 
2.8%
36416
 
2.7%
Other values (339)10652
69.4%
ValueCountFrequency (%)
0110
0.7%
512
 
0.1%
620
 
0.1%
736
 
0.2%
820
 
0.1%
964
0.4%
1056
0.4%
10.74
 
< 0.1%
10.84
 
< 0.1%
10.94
 
< 0.1%
ValueCountFrequency (%)
894
< 0.1%
76.54
< 0.1%
764
< 0.1%
754
< 0.1%
74.34
< 0.1%
73.94
< 0.1%
73.24
< 0.1%
734
< 0.1%
71.94
< 0.1%
71.44
< 0.1%
2022-10-20T18:53:39.789639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41508
 
3.3%
40508
 
3.3%
44504
 
3.3%
45496
 
3.2%
42476
 
3.1%
47468
 
3.1%
43460
 
3.0%
46432
 
2.8%
38424
 
2.8%
36416
 
2.7%
Other values (339)10652
69.4%
ValueCountFrequency (%)
0110
0.7%
512
 
0.1%
620
 
0.1%
736
 
0.2%
820
 
0.1%
964
0.4%
1056
0.4%
10.74
 
< 0.1%
10.84
 
< 0.1%
10.94
 
< 0.1%
ValueCountFrequency (%)
894
< 0.1%
76.54
< 0.1%
764
< 0.1%
754
< 0.1%
74.34
< 0.1%
73.94
< 0.1%
73.24
< 0.1%
734
< 0.1%
71.94
< 0.1%
71.44
< 0.1%
2022-10-20T19:31:26.949343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Hour
Numeric time series

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.0015641293013555788
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean14.479535974973931
Minimum0
Maximum23
Zeros1930
Zeros (%)0.1257820646506778
Memory size122880
2022-10-20T18:53:40.022708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Hour
Numeric time series

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.0015641293013555788
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean14.479535974973931
Minimum0
Maximum23
Zeros1930
Zeros (%)0.1257820646506778
Memory size122880
2022-10-20T19:31:27.086347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median19
Q320
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.997072944
Coefficient of variation (CV)0.5523017421
Kurtosis-1.030692376
Mean14.47953597
Median Absolute Deviation (MAD)2
Skewness-0.7864217587
Sum222174
Variance63.95317567
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.749391267 × 10-28
2022-10-20T18:53:40.158008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median19
Q320
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.997072944
Coefficient of variation (CV)0.5523017421
Kurtosis-1.030692376
Mean14.47953597
Median Absolute Deviation (MAD)2
Skewness-0.7864217587
Sum222174
Variance63.95317567
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.749391267 × 10-28
2022-10-20T19:31:27.167641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
202276
14.8%
192168
14.1%
212144
14.0%
01930
12.6%
181500
9.8%
221052
6.9%
6828
 
5.4%
23630
 
4.1%
7576
 
3.8%
8364
 
2.4%
Other values (14)1876
12.2%
ValueCountFrequency (%)
01930
12.6%
1268
 
1.7%
2136
 
0.9%
3104
 
0.7%
488
 
0.6%
5344
 
2.2%
6828
5.4%
7576
 
3.8%
8364
 
2.4%
9336
 
2.2%
ValueCountFrequency (%)
23630
 
4.1%
221052
6.9%
212144
14.0%
202276
14.8%
192168
14.1%
181500
9.8%
17200
 
1.3%
1632
 
0.2%
1520
 
0.1%
1420
 
0.1%
2022-10-20T18:53:40.473939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
202276
14.8%
192168
14.1%
212144
14.0%
01930
12.6%
181500
9.8%
221052
6.9%
6828
 
5.4%
23630
 
4.1%
7576
 
3.8%
8364
 
2.4%
Other values (14)1876
12.2%
ValueCountFrequency (%)
01930
12.6%
1268
 
1.7%
2136
 
0.9%
3104
 
0.7%
488
 
0.6%
5344
 
2.2%
6828
5.4%
7576
 
3.8%
8364
 
2.4%
9336
 
2.2%
ValueCountFrequency (%)
23630
 
4.1%
221052
6.9%
212144
14.0%
202276
14.8%
192168
14.1%
181500
9.8%
17200
 
1.3%
1632
 
0.2%
1520
 
0.1%
1420
 
0.1%
2022-10-20T19:31:27.386427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct70
Distinct (%)0.004562043795620438
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean36.03050052137643
Minimum0
Maximum88
Zeros110
Zeros (%)0.007168925964546402
Memory size122880
2022-10-20T18:53:40.711844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct70
Distinct (%)0.004562043795620438
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean36.03050052137643
Minimum0
Maximum88
Zeros110
Zeros (%)0.007168925964546402
Memory size122880
2022-10-20T19:31:27.515386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q128
median38
Q344
95-th percentile54
Maximum88
Range88
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.39992128
Coefficient of variation (CV)0.3441506806
Kurtosis0.07210714121
Mean36.03050052
Median Absolute Deviation (MAD)7
Skewness-0.3412746982
Sum552852
Variance153.7580477
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value6.252793809 × 10-14
2022-10-20T18:53:40.880170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q128
median38
Q344
95-th percentile54
Maximum88
Range88
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.39992128
Coefficient of variation (CV)0.3441506806
Kurtosis0.07210714121
Mean36.03050052
Median Absolute Deviation (MAD)7
Skewness-0.3412746982
Sum552852
Variance153.7580477
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value6.252793809 × 10-14
2022-10-20T19:31:27.606425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
421124
 
7.3%
39548
 
3.6%
40544
 
3.5%
38540
 
3.5%
44540
 
3.5%
41532
 
3.5%
25524
 
3.4%
36496
 
3.2%
45480
 
3.1%
43472
 
3.1%
Other values (60)9544
62.2%
ValueCountFrequency (%)
0110
0.7%
512
 
0.1%
620
 
0.1%
736
 
0.2%
884
0.5%
968
0.4%
10152
1.0%
11108
0.7%
1292
0.6%
13108
0.7%
ValueCountFrequency (%)
884
 
< 0.1%
748
 
0.1%
734
 
< 0.1%
724
 
< 0.1%
7112
0.1%
6916
0.1%
688
 
0.1%
6712
0.1%
6624
0.2%
6524
0.2%
2022-10-20T18:53:41.367237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
421124
 
7.3%
39548
 
3.6%
40544
 
3.5%
38540
 
3.5%
44540
 
3.5%
41532
 
3.5%
25524
 
3.4%
36496
 
3.2%
45480
 
3.1%
43472
 
3.1%
Other values (60)9544
62.2%
ValueCountFrequency (%)
0110
0.7%
512
 
0.1%
620
 
0.1%
736
 
0.2%
884
0.5%
968
0.4%
10152
1.0%
11108
0.7%
1292
0.6%
13108
0.7%
ValueCountFrequency (%)
884
 
< 0.1%
748
 
0.1%
734
 
< 0.1%
724
 
< 0.1%
7112
0.1%
6916
0.1%
688
 
0.1%
6712
0.1%
6624
0.2%
6524
0.2%
2022-10-20T19:31:27.798782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Parts per million
15344 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters260848
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million15344
100.0%

Length

2022-10-20T18:53:41.693869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Parts per million
15344 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters260848
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million15344
100.0%

Length

2022-10-20T19:31:27.933276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:53:41.846794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:28.010603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts15344
33.3%
per15344
33.3%
million15344
33.3%

Most occurring characters

ValueCountFrequency (%)
r30688
11.8%
30688
11.8%
i30688
11.8%
l30688
11.8%
P15344
 
5.9%
a15344
 
5.9%
t15344
 
5.9%
s15344
 
5.9%
p15344
 
5.9%
e15344
 
5.9%
Other values (3)46032
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter214816
82.4%
Space Separator30688
 
11.8%
Uppercase Letter15344
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r30688
14.3%
i30688
14.3%
l30688
14.3%
a15344
7.1%
t15344
7.1%
s15344
7.1%
p15344
7.1%
e15344
7.1%
m15344
7.1%
o15344
7.1%
Space Separator
ValueCountFrequency (%)
30688
100.0%
Uppercase Letter
ValueCountFrequency (%)
P15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin230160
88.2%
Common30688
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r30688
13.3%
i30688
13.3%
l30688
13.3%
P15344
6.7%
a15344
6.7%
t15344
6.7%
s15344
6.7%
p15344
6.7%
e15344
6.7%
m15344
6.7%
Other values (2)30688
13.3%
Common
ValueCountFrequency (%)
30688
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII260848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r30688
11.8%
30688
11.8%
i30688
11.8%
l30688
11.8%
P15344
 
5.9%
a15344
 
5.9%
t15344
 
5.9%
s15344
 
5.9%
p15344
 
5.9%
e15344
 
5.9%
Other values (3)46032
17.6%

O3 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1085
Distinct (%)0.07071167883211679
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.026245675703858187
Minimum0.0005
Maximum0.06175
Zeros0
Zeros (%)0.0
Memory size122880
2022-10-20T18:53:41.971563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts15344
33.3%
per15344
33.3%
million15344
33.3%

Most occurring characters

ValueCountFrequency (%)
r30688
11.8%
30688
11.8%
i30688
11.8%
l30688
11.8%
P15344
 
5.9%
a15344
 
5.9%
t15344
 
5.9%
s15344
 
5.9%
p15344
 
5.9%
e15344
 
5.9%
Other values (3)46032
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter214816
82.4%
Space Separator30688
 
11.8%
Uppercase Letter15344
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r30688
14.3%
i30688
14.3%
l30688
14.3%
a15344
7.1%
t15344
7.1%
s15344
7.1%
p15344
7.1%
e15344
7.1%
m15344
7.1%
o15344
7.1%
Space Separator
ValueCountFrequency (%)
30688
100.0%
Uppercase Letter
ValueCountFrequency (%)
P15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin230160
88.2%
Common30688
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r30688
13.3%
i30688
13.3%
l30688
13.3%
P15344
6.7%
a15344
6.7%
t15344
6.7%
s15344
6.7%
p15344
6.7%
e15344
6.7%
m15344
6.7%
Other values (2)30688
13.3%
Common
ValueCountFrequency (%)
30688
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII260848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r30688
11.8%
30688
11.8%
i30688
11.8%
l30688
11.8%
P15344
 
5.9%
a15344
 
5.9%
t15344
 
5.9%
s15344
 
5.9%
p15344
 
5.9%
e15344
 
5.9%
Other values (3)46032
17.6%

O3 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1085
Distinct (%)0.07071167883211679
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.026245675703858187
Minimum0.0005
Maximum0.06175
Zeros0
Zeros (%)0.0
Memory size122880
2022-10-20T19:31:28.072088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.0005
5-th percentile0.0080063
Q10.016833
median0.026458
Q30.034833
95-th percentile0.0454107
Maximum0.06175
Range0.06125
Interquartile range (IQR)0.018

Descriptive statistics

Standard deviation0.01159147458
Coefficient of variation (CV)0.4416527398
Kurtosis-0.7222908741
Mean0.0262456757
Median Absolute Deviation (MAD)0.009042
Skewness0.1051302205
Sum402.713648
Variance0.000134362283
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.158947717 × 10-6
2022-10-20T18:53:42.145879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.0005
5-th percentile0.0080063
Q10.016833
median0.026458
Q30.034833
95-th percentile0.0454107
Maximum0.06175
Range0.06125
Interquartile range (IQR)0.018

Descriptive statistics

Standard deviation0.01159147458
Coefficient of variation (CV)0.4416527398
Kurtosis-0.7222908741
Mean0.0262456757
Median Absolute Deviation (MAD)0.009042
Skewness0.1051302205
Sum402.713648
Variance0.000134362283
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.158947717 × 10-6
2022-10-20T19:31:28.165661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02691764
 
0.4%
0.02004260
 
0.4%
0.02987552
 
0.3%
0.0342548
 
0.3%
0.03608348
 
0.3%
0.02187544
 
0.3%
0.02754240
 
0.3%
0.0317540
 
0.3%
0.02995840
 
0.3%
0.03333340
 
0.3%
Other values (1075)14868
96.9%
ValueCountFrequency (%)
0.00054
< 0.1%
0.0007924
< 0.1%
0.0009174
< 0.1%
0.0011674
< 0.1%
0.0015424
< 0.1%
0.0018334
< 0.1%
0.0024
< 0.1%
0.0021258
0.1%
0.0024584
< 0.1%
0.00254
< 0.1%
ValueCountFrequency (%)
0.061754
< 0.1%
0.059754
< 0.1%
0.0582924
< 0.1%
0.0574174
< 0.1%
0.0571258
0.1%
0.0569178
0.1%
0.0559584
< 0.1%
0.0557924
< 0.1%
0.055754
< 0.1%
0.0557084
< 0.1%
2022-10-20T18:53:43.147208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02691764
 
0.4%
0.02004260
 
0.4%
0.02987552
 
0.3%
0.0342548
 
0.3%
0.03608348
 
0.3%
0.02187544
 
0.3%
0.02754240
 
0.3%
0.0317540
 
0.3%
0.02995840
 
0.3%
0.03333340
 
0.3%
Other values (1075)14868
96.9%
ValueCountFrequency (%)
0.00054
< 0.1%
0.0007924
< 0.1%
0.0009174
< 0.1%
0.0011674
< 0.1%
0.0015424
< 0.1%
0.0018334
< 0.1%
0.0024
< 0.1%
0.0021258
0.1%
0.0024584
< 0.1%
0.00254
< 0.1%
ValueCountFrequency (%)
0.061754
< 0.1%
0.059754
< 0.1%
0.0582924
< 0.1%
0.0574174
< 0.1%
0.0571258
0.1%
0.0569178
0.1%
0.0559584
< 0.1%
0.0557924
< 0.1%
0.055754
< 0.1%
0.0557084
< 0.1%
2022-10-20T19:31:28.407125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct83
Distinct (%)0.005409280500521377
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.04622406152241918
Minimum0.001
Maximum0.085
Zeros0
Zeros (%)0.0
Memory size122880
2022-10-20T18:53:43.406343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct83
Distinct (%)0.005409280500521377
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.04622406152241918
Minimum0.001
Maximum0.085
Zeros0
Zeros (%)0.0
Memory size122880
2022-10-20T19:31:28.542765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.02
Q10.036
median0.047
Q30.056
95-th percentile0.068
Maximum0.085
Range0.084
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.01454120911
Coefficient of variation (CV)0.3145809484
Kurtosis-0.275076831
Mean0.04622406152
Median Absolute Deviation (MAD)0.01
Skewness-0.2798089849
Sum709.262
Variance0.0002114467625
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.863703613 × 10-6
2022-10-20T18:53:43.599244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.02
Q10.036
median0.047
Q30.056
95-th percentile0.068
Maximum0.085
Range0.084
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.01454120911
Coefficient of variation (CV)0.3145809484
Kurtosis-0.275076831
Mean0.04622406152
Median Absolute Deviation (MAD)0.01
Skewness-0.2798089849
Sum709.262
Variance0.0002114467625
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.863703613 × 10-6
2022-10-20T19:31:28.642448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.052488
 
3.2%
0.055488
 
3.2%
0.051472
 
3.1%
0.046468
 
3.1%
0.056428
 
2.8%
0.053414
 
2.7%
0.049404
 
2.6%
0.054404
 
2.6%
0.044392
 
2.6%
0.047388
 
2.5%
Other values (73)10998
71.7%
ValueCountFrequency (%)
0.0014
 
< 0.1%
0.00216
0.1%
0.0044
 
< 0.1%
0.0054
 
< 0.1%
0.00620
0.1%
0.00716
0.1%
0.00816
0.1%
0.00932
0.2%
0.0132
0.2%
0.01128
0.2%
ValueCountFrequency (%)
0.0858
 
0.1%
0.08416
 
0.1%
0.0838
 
0.1%
0.0818
 
0.1%
0.088
 
0.1%
0.07924
 
0.2%
0.07816
 
0.1%
0.07736
0.2%
0.07664
0.4%
0.07568
0.4%
2022-10-20T18:53:44.169480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.052488
 
3.2%
0.055488
 
3.2%
0.051472
 
3.1%
0.046468
 
3.1%
0.056428
 
2.8%
0.053414
 
2.7%
0.049404
 
2.6%
0.054404
 
2.6%
0.044392
 
2.6%
0.047388
 
2.5%
Other values (73)10998
71.7%
ValueCountFrequency (%)
0.0014
 
< 0.1%
0.00216
0.1%
0.0044
 
< 0.1%
0.0054
 
< 0.1%
0.00620
0.1%
0.00716
0.1%
0.00816
0.1%
0.00932
0.2%
0.0132
0.2%
0.01128
0.2%
ValueCountFrequency (%)
0.0858
 
0.1%
0.08416
 
0.1%
0.0838
 
0.1%
0.0818
 
0.1%
0.088
 
0.1%
0.07924
 
0.2%
0.07816
 
0.1%
0.07736
0.2%
0.07664
0.4%
0.07568
0.4%
2022-10-20T19:31:28.908268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Hour
Numeric time series

HIGH CORRELATION

Distinct22
Distinct (%)0.0014337851929092805
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean10.289233576642335
Minimum0
Maximum23
Zeros76
Zeros (%)0.004953076120959333
Memory size122880
2022-10-20T18:53:44.421976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Hour
Numeric time series

HIGH CORRELATION

Distinct22
Distinct (%)0.0014337851929092805
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean10.289233576642335
Minimum0
Maximum23
Zeros76
Zeros (%)0.004953076120959333
Memory size122880
2022-10-20T19:31:29.035132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q110
median10
Q311
95-th percentile12
Maximum23
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.878995777
Coefficient of variation (CV)0.1826176618
Kurtosis22.67354598
Mean10.28923358
Median Absolute Deviation (MAD)1
Skewness2.516658977
Sum157878
Variance3.530625131
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.932374751 × 10-28
2022-10-20T18:53:44.564492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q110
median10
Q311
95-th percentile12
Maximum23
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.878995777
Coefficient of variation (CV)0.1826176618
Kurtosis22.67354598
Mean10.28923358
Median Absolute Deviation (MAD)1
Skewness2.516658977
Sum157878
Variance3.530625131
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.932374751 × 10-28
2022-10-20T19:31:29.119310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
107462
48.6%
113870
25.2%
92624
 
17.1%
12468
 
3.1%
8248
 
1.6%
13128
 
0.8%
076
 
0.5%
2368
 
0.4%
2052
 
0.3%
1448
 
0.3%
Other values (12)300
 
2.0%
ValueCountFrequency (%)
076
 
0.5%
18
 
0.1%
34
 
< 0.1%
48
 
0.1%
628
 
0.2%
736
 
0.2%
8248
 
1.6%
92624
 
17.1%
107462
48.6%
113870
25.2%
ValueCountFrequency (%)
2368
0.4%
2240
0.3%
2144
0.3%
2052
0.3%
1932
0.2%
1820
 
0.1%
1728
0.2%
1632
0.2%
1520
 
0.1%
1448
0.3%
2022-10-20T18:53:45.098215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
107462
48.6%
113870
25.2%
92624
 
17.1%
12468
 
3.1%
8248
 
1.6%
13128
 
0.8%
076
 
0.5%
2368
 
0.4%
2052
 
0.3%
1448
 
0.3%
Other values (12)300
 
2.0%
ValueCountFrequency (%)
076
 
0.5%
18
 
0.1%
34
 
< 0.1%
48
 
0.1%
628
 
0.2%
736
 
0.2%
8248
 
1.6%
92624
 
17.1%
107462
48.6%
113870
25.2%
ValueCountFrequency (%)
2368
0.4%
2240
0.3%
2144
0.3%
2052
0.3%
1932
0.2%
1820
 
0.1%
1728
0.2%
1632
0.2%
1520
 
0.1%
1448
0.3%
2022-10-20T19:31:29.310352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct84
Distinct (%)0.005474452554744526
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean43.418013555787276
Minimum1
Maximum147
Zeros0
Zeros (%)0.0
Memory size122880
2022-10-20T18:53:45.343499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct84
Distinct (%)0.005474452554744526
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean43.418013555787276
Minimum1
Maximum147
Zeros0
Zeros (%)0.0
Memory size122880
2022-10-20T19:31:30.206610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q131
median42
Q349
95-th percentile84
Maximum147
Range146
Interquartile range (IQR)18

Descriptive statistics

Standard deviation19.3097483
Coefficient of variation (CV)0.4447404826
Kurtosis2.378396826
Mean43.41801356
Median Absolute Deviation (MAD)9
Skewness1.2410603
Sum666206
Variance372.8663795
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.102256766 × 10-10
2022-10-20T18:53:45.526791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q131
median42
Q349
95-th percentile84
Maximum147
Range146
Interquartile range (IQR)18

Descriptive statistics

Standard deviation19.3097483
Coefficient of variation (CV)0.4447404826
Kurtosis2.378396826
Mean43.41801356
Median Absolute Deviation (MAD)9
Skewness1.2410603
Sum666206
Variance372.8663795
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.102256766 × 10-10
2022-10-20T19:31:30.307039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47872
 
5.7%
42648
 
4.2%
36604
 
3.9%
31600
 
3.9%
44570
 
3.7%
46448
 
2.9%
43436
 
2.8%
39436
 
2.8%
45390
 
2.5%
48384
 
2.5%
Other values (74)9956
64.9%
ValueCountFrequency (%)
14
 
< 0.1%
216
 
0.1%
34
 
< 0.1%
44
 
< 0.1%
516
 
0.1%
620
 
0.1%
716
 
0.1%
864
0.4%
928
0.2%
1012
 
0.1%
ValueCountFrequency (%)
1474
 
< 0.1%
1364
 
< 0.1%
1334
 
< 0.1%
1294
 
< 0.1%
1268
 
0.1%
1248
 
0.1%
12228
0.2%
11916
0.1%
1154
 
< 0.1%
1144
 
< 0.1%
2022-10-20T18:53:46.796963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47872
 
5.7%
42648
 
4.2%
36604
 
3.9%
31600
 
3.9%
44570
 
3.7%
46448
 
2.9%
43436
 
2.8%
39436
 
2.8%
45390
 
2.5%
48384
 
2.5%
Other values (74)9956
64.9%
ValueCountFrequency (%)
14
 
< 0.1%
216
 
0.1%
34
 
< 0.1%
44
 
< 0.1%
516
 
0.1%
620
 
0.1%
716
 
0.1%
864
0.4%
928
0.2%
1012
 
0.1%
ValueCountFrequency (%)
1474
 
< 0.1%
1364
 
< 0.1%
1334
 
< 0.1%
1294
 
< 0.1%
1268
 
0.1%
1248
 
0.1%
12228
0.2%
11916
0.1%
1154
 
< 0.1%
1144
 
< 0.1%
2022-10-20T19:31:30.465809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Parts per billion
15344 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters260848
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion15344
100.0%

Length

2022-10-20T18:53:47.042503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Parts per billion
15344 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters260848
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion15344
100.0%

Length

2022-10-20T19:31:30.602616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:53:47.188732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:30.682130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts15344
33.3%
per15344
33.3%
billion15344
33.3%

Most occurring characters

ValueCountFrequency (%)
r30688
11.8%
30688
11.8%
i30688
11.8%
l30688
11.8%
P15344
 
5.9%
a15344
 
5.9%
t15344
 
5.9%
s15344
 
5.9%
p15344
 
5.9%
e15344
 
5.9%
Other values (3)46032
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter214816
82.4%
Space Separator30688
 
11.8%
Uppercase Letter15344
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r30688
14.3%
i30688
14.3%
l30688
14.3%
a15344
7.1%
t15344
7.1%
s15344
7.1%
p15344
7.1%
e15344
7.1%
b15344
7.1%
o15344
7.1%
Space Separator
ValueCountFrequency (%)
30688
100.0%
Uppercase Letter
ValueCountFrequency (%)
P15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin230160
88.2%
Common30688
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r30688
13.3%
i30688
13.3%
l30688
13.3%
P15344
6.7%
a15344
6.7%
t15344
6.7%
s15344
6.7%
p15344
6.7%
e15344
6.7%
b15344
6.7%
Other values (2)30688
13.3%
Common
ValueCountFrequency (%)
30688
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII260848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r30688
11.8%
30688
11.8%
i30688
11.8%
l30688
11.8%
P15344
 
5.9%
a15344
 
5.9%
t15344
 
5.9%
s15344
 
5.9%
p15344
 
5.9%
e15344
 
5.9%
Other values (3)46032
17.6%

SO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct1061
Distinct (%)0.06914754953076122
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean1.7240885741657976
Minimum0.0
Maximum6.291667
Zeros204
Zeros (%)0.013295099061522419
Memory size122880
2022-10-20T18:53:47.303335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts15344
33.3%
per15344
33.3%
billion15344
33.3%

Most occurring characters

ValueCountFrequency (%)
r30688
11.8%
30688
11.8%
i30688
11.8%
l30688
11.8%
P15344
 
5.9%
a15344
 
5.9%
t15344
 
5.9%
s15344
 
5.9%
p15344
 
5.9%
e15344
 
5.9%
Other values (3)46032
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter214816
82.4%
Space Separator30688
 
11.8%
Uppercase Letter15344
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r30688
14.3%
i30688
14.3%
l30688
14.3%
a15344
7.1%
t15344
7.1%
s15344
7.1%
p15344
7.1%
e15344
7.1%
b15344
7.1%
o15344
7.1%
Space Separator
ValueCountFrequency (%)
30688
100.0%
Uppercase Letter
ValueCountFrequency (%)
P15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin230160
88.2%
Common30688
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r30688
13.3%
i30688
13.3%
l30688
13.3%
P15344
6.7%
a15344
6.7%
t15344
6.7%
s15344
6.7%
p15344
6.7%
e15344
6.7%
b15344
6.7%
Other values (2)30688
13.3%
Common
ValueCountFrequency (%)
30688
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII260848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r30688
11.8%
30688
11.8%
i30688
11.8%
l30688
11.8%
P15344
 
5.9%
a15344
 
5.9%
t15344
 
5.9%
s15344
 
5.9%
p15344
 
5.9%
e15344
 
5.9%
Other values (3)46032
17.6%

SO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct1061
Distinct (%)0.06914754953076122
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean1.7240885741657976
Minimum0.0
Maximum6.291667
Zeros204
Zeros (%)0.013295099061522419
Memory size122880
2022-10-20T19:31:30.743726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.9344205
median1.6535715
Q32.333333
95-th percentile3.6119294
Maximum6.291667
Range6.291667
Interquartile range (IQR)1.3989125

Descriptive statistics

Standard deviation1.038770279
Coefficient of variation (CV)0.6025040098
Kurtosis0.5159210577
Mean1.724088574
Median Absolute Deviation (MAD)0.7089285
Skewness0.6888250708
Sum26454.41508
Variance1.079043693
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.903338346 × 10-5
2022-10-20T18:53:47.477672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.9344205
median1.6535715
Q32.333333
95-th percentile3.6119294
Maximum6.291667
Range6.291667
Interquartile range (IQR)1.3989125

Descriptive statistics

Standard deviation1.038770279
Coefficient of variation (CV)0.6025040098
Kurtosis0.5159210577
Mean1.724088574
Median Absolute Deviation (MAD)0.7089285
Skewness0.6888250708
Sum26454.41508
Variance1.079043693
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.903338346 × 10-5
2022-10-20T19:31:30.839781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2898
 
5.9%
3268
 
1.7%
0204
 
1.3%
1134
 
0.9%
2.25104
 
0.7%
2.04166798
 
0.6%
1.70833396
 
0.6%
1.7590
 
0.6%
2.87584
 
0.5%
2.37582
 
0.5%
Other values (1051)13286
86.6%
ValueCountFrequency (%)
0204
1.3%
0.0043482
 
< 0.1%
0.0083332
 
< 0.1%
0.012512
 
0.1%
0.0208334
 
< 0.1%
0.02516
 
0.1%
0.0291672
 
< 0.1%
0.037540
 
0.3%
0.04166732
 
0.2%
0.0514
 
0.1%
ValueCountFrequency (%)
6.2916672
 
< 0.1%
6.26252
 
< 0.1%
5.9583332
 
< 0.1%
5.9252
 
< 0.1%
5.7272732
 
< 0.1%
5.72
 
< 0.1%
5.6666676
< 0.1%
5.654
 
< 0.1%
5.63752
 
< 0.1%
5.58333310
0.1%
2022-10-20T18:53:47.861646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2898
 
5.9%
3268
 
1.7%
0204
 
1.3%
1134
 
0.9%
2.25104
 
0.7%
2.04166798
 
0.6%
1.70833396
 
0.6%
1.7590
 
0.6%
2.87584
 
0.5%
2.37582
 
0.5%
Other values (1051)13286
86.6%
ValueCountFrequency (%)
0204
1.3%
0.0043482
 
< 0.1%
0.0083332
 
< 0.1%
0.012512
 
0.1%
0.0208334
 
< 0.1%
0.02516
 
0.1%
0.0291672
 
< 0.1%
0.037540
 
0.3%
0.04166732
 
0.2%
0.0514
 
0.1%
ValueCountFrequency (%)
6.2916672
 
< 0.1%
6.26252
 
< 0.1%
5.9583332
 
< 0.1%
5.9252
 
< 0.1%
5.7272732
 
< 0.1%
5.72
 
< 0.1%
5.6666676
< 0.1%
5.654
 
< 0.1%
5.63752
 
< 0.1%
5.58333310
0.1%
2022-10-20T19:31:31.021299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct81
Distinct (%)0.005278936392075078
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean2.5849648070907194
Minimum0.0
Maximum28.0
Zeros204
Zeros (%)0.013295099061522419
Memory size122880
2022-10-20T18:53:48.098700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct81
Distinct (%)0.005278936392075078
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean2.5849648070907194
Minimum0.0
Maximum28.0
Zeros204
Zeros (%)0.013295099061522419
Memory size122880
2022-10-20T19:31:31.151856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.7
Q11.6
median2
Q33
95-th percentile6
Maximum28
Range28
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.554459714
Coefficient of variation (CV)0.6013465676
Kurtosis12.47821368
Mean2.584964807
Median Absolute Deviation (MAD)1
Skewness1.837202739
Sum39663.7
Variance2.416345003
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value8.92340525 × 10-8
2022-10-20T18:53:48.277487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.7
Q11.6
median2
Q33
95-th percentile6
Maximum28
Range28
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.554459714
Coefficient of variation (CV)0.6013465676
Kurtosis12.47821368
Mean2.584964807
Median Absolute Deviation (MAD)1
Skewness1.837202739
Sum39663.7
Variance2.416345003
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value8.92340525 × 10-8
2022-10-20T19:31:31.265561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23194
20.8%
32458
16.0%
4928
 
6.0%
1701
 
4.6%
5520
 
3.4%
1.6438
 
2.9%
1.3388
 
2.5%
2.6360
 
2.3%
2.3354
 
2.3%
1.1279
 
1.8%
Other values (71)5724
37.3%
ValueCountFrequency (%)
0204
1.3%
0.132
 
0.2%
0.260
 
0.4%
0.390
 
0.6%
0.494
 
0.6%
0.584
 
0.5%
0.6186
1.2%
0.7162
1.1%
0.8251
1.6%
0.9250
1.6%
ValueCountFrequency (%)
282
 
< 0.1%
192
 
< 0.1%
112
 
< 0.1%
10.62
 
< 0.1%
108
0.1%
9.62
 
< 0.1%
9.12
 
< 0.1%
918
0.1%
8.32
 
< 0.1%
8.12
 
< 0.1%
2022-10-20T18:53:48.784185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23194
20.8%
32458
16.0%
4928
 
6.0%
1701
 
4.6%
5520
 
3.4%
1.6438
 
2.9%
1.3388
 
2.5%
2.6360
 
2.3%
2.3354
 
2.3%
1.1279
 
1.8%
Other values (71)5724
37.3%
ValueCountFrequency (%)
0204
1.3%
0.132
 
0.2%
0.260
 
0.4%
0.390
 
0.6%
0.494
 
0.6%
0.584
 
0.5%
0.6186
1.2%
0.7162
1.1%
0.8251
1.6%
0.9250
1.6%
ValueCountFrequency (%)
282
 
< 0.1%
192
 
< 0.1%
112
 
< 0.1%
10.62
 
< 0.1%
108
0.1%
9.62
 
< 0.1%
9.12
 
< 0.1%
918
0.1%
8.32
 
< 0.1%
8.12
 
< 0.1%
2022-10-20T19:31:31.448923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Hour
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct24
Distinct (%)0.0015641293013555788
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean9.52463503649635
Minimum0
Maximum23
Zeros2418
Zeros (%)0.15758602711157454
Memory size122880
2022-10-20T18:53:49.088907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Hour
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct24
Distinct (%)0.0015641293013555788
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean9.52463503649635
Minimum0
Maximum23
Zeros2418
Zeros (%)0.15758602711157454
Memory size122880
2022-10-20T19:31:31.576223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q317
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.071337931
Coefficient of variation (CV)0.8474170296
Kurtosis-1.138485804
Mean9.524635036
Median Absolute Deviation (MAD)6
Skewness0.4837370467
Sum146146
Variance65.14649599
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value8.605141039 × 10-20
2022-10-20T18:53:49.235721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q317
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.071337931
Coefficient of variation (CV)0.8474170296
Kurtosis-1.138485804
Mean9.524635036
Median Absolute Deviation (MAD)6
Skewness0.4837370467
Sum146146
Variance65.14649599
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value8.605141039 × 10-20
2022-10-20T19:31:31.664340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
22427
15.8%
02418
15.8%
82119
13.8%
231928
12.6%
111133
7.4%
20690
 
4.5%
7628
 
4.1%
14474
 
3.1%
21454
 
3.0%
5406
 
2.6%
Other values (14)2667
17.4%
ValueCountFrequency (%)
02418
15.8%
1249
 
1.6%
22427
15.8%
3104
 
0.7%
474
 
0.5%
5406
 
2.6%
6346
 
2.3%
7628
 
4.1%
82119
13.8%
9366
 
2.4%
ValueCountFrequency (%)
231928
12.6%
22374
 
2.4%
21454
 
3.0%
20690
 
4.5%
19172
 
1.1%
1884
 
0.5%
17230
 
1.5%
1632
 
0.2%
1572
 
0.5%
14474
 
3.1%
2022-10-20T18:53:49.656271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
22427
15.8%
02418
15.8%
82119
13.8%
231928
12.6%
111133
7.4%
20690
 
4.5%
7628
 
4.1%
14474
 
3.1%
21454
 
3.0%
5406
 
2.6%
Other values (14)2667
17.4%
ValueCountFrequency (%)
02418
15.8%
1249
 
1.6%
22427
15.8%
3104
 
0.7%
474
 
0.5%
5406
 
2.6%
6346
 
2.3%
7628
 
4.1%
82119
13.8%
9366
 
2.4%
ValueCountFrequency (%)
231928
12.6%
22374
 
2.4%
21454
 
3.0%
20690
 
4.5%
19172
 
1.1%
1884
 
0.5%
17230
 
1.5%
1632
 
0.2%
1572
 
0.5%
14474
 
3.1%
2022-10-20T19:31:31.900307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 AQI
Numeric time series

HIGH CORRELATION
MISSING
NON STATIONARY
SEASONAL
ZEROS

Distinct14
Distinct (%)0.0018248175182481751
Missing7672
Missing (%)0.5
Infinite0
Infinite (%)0.0
Mean3.630865484880083
Minimum0.0
Maximum40.0
Zeros520
Zeros (%)0.03388946819603754
Memory size122880
2022-10-20T18:53:49.916333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 AQI
Numeric time series

HIGH CORRELATION
MISSING
NON STATIONARY
SEASONAL
ZEROS

Distinct14
Distinct (%)0.0018248175182481751
Missing7672
Missing (%)0.5
Infinite0
Infinite (%)0.0
Mean3.630865484880083
Minimum0.0
Maximum40.0
Zeros520
Zeros (%)0.03388946819603754
Memory size122880
2022-10-20T19:31:32.039571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile9
Maximum40
Range40
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.591428443
Coefficient of variation (CV)0.7137219635
Kurtosis12.4500071
Mean3.630865485
Median Absolute Deviation (MAD)1
Skewness1.779308081
Sum27856
Variance6.715501376
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0001461336727
2022-10-20T18:53:50.080180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile9
Maximum40
Range40
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.591428443
Coefficient of variation (CV)0.7137219635
Kurtosis12.4500071
Mean3.630865485
Median Absolute Deviation (MAD)1
Skewness1.779308081
Sum27856
Variance6.715501376
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0001461336727
2022-10-20T19:31:32.120902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
32316
 
15.1%
41702
 
11.1%
11442
 
9.4%
6790
 
5.1%
0520
 
3.4%
7386
 
2.5%
9224
 
1.5%
10176
 
1.1%
1182
 
0.5%
1320
 
0.1%
Other values (4)14
 
0.1%
(Missing)7672
50.0%
ValueCountFrequency (%)
0520
 
3.4%
11442
9.4%
32316
15.1%
41702
11.1%
6790
 
5.1%
7386
 
2.5%
9224
 
1.5%
10176
 
1.1%
1182
 
0.5%
1320
 
0.1%
ValueCountFrequency (%)
402
 
< 0.1%
272
 
< 0.1%
162
 
< 0.1%
148
 
0.1%
1320
 
0.1%
1182
 
0.5%
10176
 
1.1%
9224
 
1.5%
7386
2.5%
6790
5.1%
2022-10-20T18:53:50.352204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
32316
 
15.1%
41702
 
11.1%
11442
 
9.4%
6790
 
5.1%
0520
 
3.4%
7386
 
2.5%
9224
 
1.5%
10176
 
1.1%
1182
 
0.5%
1320
 
0.1%
Other values (4)14
 
0.1%
(Missing)7672
50.0%
ValueCountFrequency (%)
0520
 
3.4%
11442
9.4%
32316
15.1%
41702
11.1%
6790
 
5.1%
7386
 
2.5%
9224
 
1.5%
10176
 
1.1%
1182
 
0.5%
1320
 
0.1%
ValueCountFrequency (%)
402
 
< 0.1%
272
 
< 0.1%
162
 
< 0.1%
148
 
0.1%
1320
 
0.1%
1182
 
0.5%
10176
 
1.1%
9224
 
1.5%
7386
2.5%
6790
5.1%
2022-10-20T19:31:32.302145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Parts per million
15344 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters260848
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million15344
100.0%

Length

2022-10-20T18:53:50.596332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.0 KiB
Parts per million
15344 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters260848
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million15344
100.0%

Length

2022-10-20T19:31:32.434548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:53:50.726820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:31:32.507601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts15344
33.3%
per15344
33.3%
million15344
33.3%

Most occurring characters

ValueCountFrequency (%)
r30688
11.8%
30688
11.8%
i30688
11.8%
l30688
11.8%
P15344
 
5.9%
a15344
 
5.9%
t15344
 
5.9%
s15344
 
5.9%
p15344
 
5.9%
e15344
 
5.9%
Other values (3)46032
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter214816
82.4%
Space Separator30688
 
11.8%
Uppercase Letter15344
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r30688
14.3%
i30688
14.3%
l30688
14.3%
a15344
7.1%
t15344
7.1%
s15344
7.1%
p15344
7.1%
e15344
7.1%
m15344
7.1%
o15344
7.1%
Space Separator
ValueCountFrequency (%)
30688
100.0%
Uppercase Letter
ValueCountFrequency (%)
P15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin230160
88.2%
Common30688
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r30688
13.3%
i30688
13.3%
l30688
13.3%
P15344
6.7%
a15344
6.7%
t15344
6.7%
s15344
6.7%
p15344
6.7%
e15344
6.7%
m15344
6.7%
Other values (2)30688
13.3%
Common
ValueCountFrequency (%)
30688
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII260848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r30688
11.8%
30688
11.8%
i30688
11.8%
l30688
11.8%
P15344
 
5.9%
a15344
 
5.9%
t15344
 
5.9%
s15344
 
5.9%
p15344
 
5.9%
e15344
 
5.9%
Other values (3)46032
17.6%

CO Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1973
Distinct (%)0.1285844629822732
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.5834522447862356
Minimum0.041667
Maximum2.6625
Zeros0
Zeros (%)0.0
Memory size122880
2022-10-20T18:53:50.843214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts15344
33.3%
per15344
33.3%
million15344
33.3%

Most occurring characters

ValueCountFrequency (%)
r30688
11.8%
30688
11.8%
i30688
11.8%
l30688
11.8%
P15344
 
5.9%
a15344
 
5.9%
t15344
 
5.9%
s15344
 
5.9%
p15344
 
5.9%
e15344
 
5.9%
Other values (3)46032
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter214816
82.4%
Space Separator30688
 
11.8%
Uppercase Letter15344
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r30688
14.3%
i30688
14.3%
l30688
14.3%
a15344
7.1%
t15344
7.1%
s15344
7.1%
p15344
7.1%
e15344
7.1%
m15344
7.1%
o15344
7.1%
Space Separator
ValueCountFrequency (%)
30688
100.0%
Uppercase Letter
ValueCountFrequency (%)
P15344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin230160
88.2%
Common30688
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r30688
13.3%
i30688
13.3%
l30688
13.3%
P15344
6.7%
a15344
6.7%
t15344
6.7%
s15344
6.7%
p15344
6.7%
e15344
6.7%
m15344
6.7%
Other values (2)30688
13.3%
Common
ValueCountFrequency (%)
30688
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII260848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r30688
11.8%
30688
11.8%
i30688
11.8%
l30688
11.8%
P15344
 
5.9%
a15344
 
5.9%
t15344
 
5.9%
s15344
 
5.9%
p15344
 
5.9%
e15344
 
5.9%
Other values (3)46032
17.6%

CO Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1973
Distinct (%)0.1285844629822732
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.5834522447862356
Minimum0.041667
Maximum2.6625
Zeros0
Zeros (%)0.0
Memory size122880
2022-10-20T19:31:32.570353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.041667
5-th percentile0.21001245
Q10.35068475
median0.495833
Q30.741667
95-th percentile1.216667
Maximum2.6625
Range2.620833
Interquartile range (IQR)0.39098225

Descriptive statistics

Standard deviation0.3265721637
Coefficient of variation (CV)0.5597238963
Kurtosis2.937856665
Mean0.5834522448
Median Absolute Deviation (MAD)0.175
Skewness1.442851454
Sum8952.491244
Variance0.1066493781
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value6.653971661 × 10-8
2022-10-20T18:53:51.030755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.041667
5-th percentile0.21001245
Q10.35068475
median0.495833
Q30.741667
95-th percentile1.216667
Maximum2.6625
Range2.620833
Interquartile range (IQR)0.39098225

Descriptive statistics

Standard deviation0.3265721637
Coefficient of variation (CV)0.5597238963
Kurtosis2.937856665
Mean0.5834522448
Median Absolute Deviation (MAD)0.175
Skewness1.442851454
Sum8952.491244
Variance0.1066493781
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value6.653971661 × 10-8
2022-10-20T19:31:32.664936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3158
 
1.0%
0.4136
 
0.9%
0.429167122
 
0.8%
0.395833116
 
0.8%
0.379167110
 
0.7%
0.516667108
 
0.7%
0.391667108
 
0.7%
0.45108
 
0.7%
0.3375106
 
0.7%
0.441667106
 
0.7%
Other values (1963)14166
92.3%
ValueCountFrequency (%)
0.0416672
< 0.1%
0.0583332
< 0.1%
0.06254
< 0.1%
0.0682922
< 0.1%
0.0705422
< 0.1%
0.0708334
< 0.1%
0.0742
< 0.1%
0.0752
< 0.1%
0.0778332
< 0.1%
0.0791672
< 0.1%
ValueCountFrequency (%)
2.66252
< 0.1%
2.5333332
< 0.1%
2.52
< 0.1%
2.4083332
< 0.1%
2.3958332
< 0.1%
2.3347832
< 0.1%
2.3333332
< 0.1%
2.3227272
< 0.1%
2.2833334
< 0.1%
2.2708332
< 0.1%
2022-10-20T18:53:51.583033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3158
 
1.0%
0.4136
 
0.9%
0.429167122
 
0.8%
0.395833116
 
0.8%
0.379167110
 
0.7%
0.516667108
 
0.7%
0.391667108
 
0.7%
0.45108
 
0.7%
0.3375106
 
0.7%
0.441667106
 
0.7%
Other values (1963)14166
92.3%
ValueCountFrequency (%)
0.0416672
< 0.1%
0.0583332
< 0.1%
0.06254
< 0.1%
0.0682922
< 0.1%
0.0705422
< 0.1%
0.0708334
< 0.1%
0.0742
< 0.1%
0.0752
< 0.1%
0.0778332
< 0.1%
0.0791672
< 0.1%
ValueCountFrequency (%)
2.66252
< 0.1%
2.5333332
< 0.1%
2.52
< 0.1%
2.4083332
< 0.1%
2.3958332
< 0.1%
2.3347832
< 0.1%
2.3333332
< 0.1%
2.3227272
< 0.1%
2.2833334
< 0.1%
2.2708332
< 0.1%
2022-10-20T19:31:32.885774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1008
Distinct (%)0.06569343065693431
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean1.0562962721584983
Minimum0.1
Maximum5.6
Zeros0
Zeros (%)0.0
Memory size122880
2022-10-20T18:53:51.839348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1008
Distinct (%)0.06569343065693431
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean1.0562962721584983
Minimum0.1
Maximum5.6
Zeros0
Zeros (%)0.0
Memory size122880
2022-10-20T19:31:33.020242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.6
median0.9
Q31.4
95-th percentile2.3
Maximum5.6
Range5.5
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.6482529376
Coefficient of variation (CV)0.6137037067
Kurtosis2.170698954
Mean1.056296272
Median Absolute Deviation (MAD)0.4
Skewness1.266638498
Sum16207.81
Variance0.4202318712
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value5.583848442 × 10-7
2022-10-20T18:53:52.017420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.6
median0.9
Q31.4
95-th percentile2.3
Maximum5.6
Range5.5
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.6482529376
Coefficient of variation (CV)0.6137037067
Kurtosis2.170698954
Mean1.056296272
Median Absolute Deviation (MAD)0.4
Skewness1.266638498
Sum16207.81
Variance0.4202318712
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value5.583848442 × 10-7
2022-10-20T19:31:33.114978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.61150
 
7.5%
0.51034
 
6.7%
0.41002
 
6.5%
0.7936
 
6.1%
0.8892
 
5.8%
0.9744
 
4.8%
1.1684
 
4.5%
1680
 
4.4%
0.3560
 
3.6%
1.2552
 
3.6%
Other values (998)7110
46.3%
ValueCountFrequency (%)
0.154
0.4%
0.1162
 
< 0.1%
0.1332
 
< 0.1%
0.1382
 
< 0.1%
0.1392
 
< 0.1%
0.1474
 
< 0.1%
0.1532
 
< 0.1%
0.1564
 
< 0.1%
0.1582
 
< 0.1%
0.1662
 
< 0.1%
ValueCountFrequency (%)
5.62
< 0.1%
5.32
< 0.1%
4.72
< 0.1%
4.62
< 0.1%
4.52
< 0.1%
4.34
< 0.1%
4.24
< 0.1%
4.12
< 0.1%
44
< 0.1%
3.92
< 0.1%
2022-10-20T18:53:52.437591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.61150
 
7.5%
0.51034
 
6.7%
0.41002
 
6.5%
0.7936
 
6.1%
0.8892
 
5.8%
0.9744
 
4.8%
1.1684
 
4.5%
1680
 
4.4%
0.3560
 
3.6%
1.2552
 
3.6%
Other values (998)7110
46.3%
ValueCountFrequency (%)
0.154
0.4%
0.1162
 
< 0.1%
0.1332
 
< 0.1%
0.1382
 
< 0.1%
0.1392
 
< 0.1%
0.1474
 
< 0.1%
0.1532
 
< 0.1%
0.1564
 
< 0.1%
0.1582
 
< 0.1%
0.1662
 
< 0.1%
ValueCountFrequency (%)
5.62
< 0.1%
5.32
< 0.1%
4.72
< 0.1%
4.62
< 0.1%
4.52
< 0.1%
4.34
< 0.1%
4.24
< 0.1%
4.12
< 0.1%
44
< 0.1%
3.92
< 0.1%
2022-10-20T19:31:33.380697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO 1st Max Hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.649374348
Minimum0
Maximum23
Zeros2898
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size120.0 KiB
2022-10-20T18:53:52.672291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO 1st Max Hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.649374348
Minimum0
Maximum23
Zeros2898
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size120.0 KiB
2022-10-20T19:31:33.516041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation9.102061787
Coefficient of variation (CV)0.943279995
Kurtosis-1.519377606
Mean9.649374348
Median Absolute Deviation (MAD)7
Skewness0.4434418543
Sum148060
Variance82.84752877
MonotonicityNot monotonic
2022-10-20T18:53:52.819312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation9.102061787
Coefficient of variation (CV)0.943279995
Kurtosis-1.519377606
Mean9.649374348
Median Absolute Deviation (MAD)7
Skewness0.4434418543
Sum148060
Variance82.84752877
MonotonicityNot monotonic
2022-10-20T19:31:33.597092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
02898
18.9%
232000
13.0%
11466
9.6%
71226
8.0%
61140
 
7.4%
211110
 
7.2%
221094
 
7.1%
21052
 
6.9%
8698
 
4.5%
20642
 
4.2%
Other values (14)2018
13.2%
ValueCountFrequency (%)
02898
18.9%
11466
9.6%
21052
 
6.9%
3470
 
3.1%
4190
 
1.2%
5414
 
2.7%
61140
 
7.4%
71226
8.0%
8698
 
4.5%
9228
 
1.5%
ValueCountFrequency (%)
232000
13.0%
221094
7.1%
211110
7.2%
20642
 
4.2%
19278
 
1.8%
1880
 
0.5%
1722
 
0.1%
1612
 
0.1%
1536
 
0.2%
146
 
< 0.1%

CO AQI
Numeric time series

HIGH CORRELATION
MISSING
NON STATIONARY
SEASONAL

Distinct37
Distinct (%)0.0048214751107636174
Missing7670
Missing (%)0.4998696558915537
Infinite0
Infinite (%)0.0
Mean10.37008079228564
Minimum1.0
Maximum42.0
Zeros0
Zeros (%)0.0
Memory size122880
2022-10-20T18:53:52.951088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
02898
18.9%
232000
13.0%
11466
9.6%
71226
8.0%
61140
 
7.4%
211110
 
7.2%
221094
 
7.1%
21052
 
6.9%
8698
 
4.5%
20642
 
4.2%
Other values (14)2018
13.2%
ValueCountFrequency (%)
02898
18.9%
11466
9.6%
21052
 
6.9%
3470
 
3.1%
4190
 
1.2%
5414
 
2.7%
61140
 
7.4%
71226
8.0%
8698
 
4.5%
9228
 
1.5%
ValueCountFrequency (%)
232000
13.0%
221094
7.1%
211110
7.2%
20642
 
4.2%
19278
 
1.8%
1880
 
0.5%
1722
 
0.1%
1612
 
0.1%
1536
 
0.2%
146
 
< 0.1%

CO AQI
Numeric time series

HIGH CORRELATION
MISSING
NON STATIONARY
SEASONAL

Distinct37
Distinct (%)0.0048214751107636174
Missing7670
Missing (%)0.4998696558915537
Infinite0
Infinite (%)0.0
Mean10.37008079228564
Minimum1.0
Maximum42.0
Zeros0
Zeros (%)0.0
Memory size122880
2022-10-20T19:31:33.668213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median9
Q314
95-th percentile23
Maximum42
Range41
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.168165229
Coefficient of variation (CV)0.5948039704
Kurtosis1.580730343
Mean10.37008079
Median Absolute Deviation (MAD)4
Skewness1.188542874
Sum79580
Variance38.04626229
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0008635620962
2022-10-20T18:53:53.095400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median9
Q314
95-th percentile23
Maximum42
Range41
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.168165229
Coefficient of variation (CV)0.5948039704
Kurtosis1.580730343
Mean10.37008079
Median Absolute Deviation (MAD)4
Skewness1.188542874
Sum79580
Variance38.04626229
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0008635620962
2022-10-20T19:31:33.754413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5826
 
5.4%
7816
 
5.3%
6766
 
5.0%
8622
 
4.1%
9564
 
3.7%
3494
 
3.2%
13448
 
2.9%
10414
 
2.7%
11402
 
2.6%
14342
 
2.2%
Other values (27)1980
 
12.9%
(Missing)7670
50.0%
ValueCountFrequency (%)
154
 
0.4%
2194
 
1.3%
3494
3.2%
5826
5.4%
6766
5.0%
7816
5.3%
8622
4.1%
9564
3.7%
10414
2.7%
11402
2.6%
ValueCountFrequency (%)
422
 
< 0.1%
412
 
< 0.1%
402
 
< 0.1%
392
 
< 0.1%
384
 
< 0.1%
362
 
< 0.1%
3510
0.1%
346
< 0.1%
3310
0.1%
3210
0.1%
2022-10-20T18:53:53.436300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5826
 
5.4%
7816
 
5.3%
6766
 
5.0%
8622
 
4.1%
9564
 
3.7%
3494
 
3.2%
13448
 
2.9%
10414
 
2.7%
11402
 
2.6%
14342
 
2.2%
Other values (27)1980
 
12.9%
(Missing)7670
50.0%
ValueCountFrequency (%)
154
 
0.4%
2194
 
1.3%
3494
3.2%
5826
5.4%
6766
5.0%
7816
5.3%
8622
4.1%
9564
3.7%
10414
2.7%
11402
2.6%
ValueCountFrequency (%)
422
 
< 0.1%
412
 
< 0.1%
402
 
< 0.1%
392
 
< 0.1%
384
 
< 0.1%
362
 
< 0.1%
3510
0.1%
346
< 0.1%
3310
0.1%
3210
0.1%
2022-10-20T19:31:33.926081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

Interactions

2022-10-20T18:53:34.056051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

Interactions

2022-10-20T19:31:23.884875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-20T18:53:53.670351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/2022-10-20T19:31:34.056994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-10-20T18:53:53.898009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-10-20T19:31:34.202389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-20T18:53:54.141388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-20T19:31:34.353954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-20T18:53:54.404371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-20T19:31:34.499559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-20T18:53:54.652868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-20T19:31:34.632583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-20T18:53:54.843491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-20T19:31:34.735770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-20T18:53:34.387378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-20T19:31:24.061650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-20T18:53:35.053358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-20T19:31:24.418708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-20T18:53:35.372762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-20T19:31:24.605619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-20T18:53:35.528467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-20T19:31:24.699550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
041399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-04Parts per billion19.25000035.0033Parts per million0.0257500.0421136Parts per billion2.1250004.006.0Parts per million0.8458332.27NaN
141399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-04Parts per billion19.25000035.0033Parts per million0.0257500.0421136Parts per billion2.1250004.006.0Parts per million1.0166671.5217.0
241399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-04Parts per billion19.25000035.0033Parts per million0.0257500.0421136Parts per billion2.0875003.62NaNParts per million0.8458332.27NaN
341399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-04Parts per billion19.25000035.0033Parts per million0.0257500.0421136Parts per billion2.0875003.62NaNParts per million1.0166671.5217.0
441399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-05Parts per billion11.45833324.0723Parts per million0.0247080.0381032Parts per billion0.5416672.063.0Parts per million0.5166670.87NaN
541399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-05Parts per billion11.45833324.0723Parts per million0.0247080.0381032Parts per billion0.5416672.063.0Parts per million0.5083330.697.0
641399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-05Parts per billion11.45833324.0723Parts per million0.0247080.0381032Parts per billion0.5125001.68NaNParts per million0.5166670.87NaN
741399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-05Parts per billion11.45833324.0723Parts per million0.0247080.0381032Parts per billion0.5125001.68NaNParts per million0.5083330.697.0
841399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-06Parts per billion16.83333333.01931Parts per million0.0190830.0391033Parts per billion1.2083334.0216.0Parts per million0.6625001.120NaN
941399974530 N 17TH AVENUEArizonaMaricopaPhoenix2005-03-06Parts per billion16.83333333.01931Parts per million0.0190830.0391033Parts per billion1.2083334.0216.0Parts per million0.6000000.92310.0

Last rows

State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
1533441399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-24Parts per billion20.19583341.02139Parts per million0.0226250.0491145Parts per billion0.2208331.191.0Parts per million0.5254171.03122NaN
1533541399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-24Parts per billion20.19583341.02139Parts per million0.0226250.0491145Parts per billion0.2208331.191.0Parts per million0.4583330.60057.0
1533641399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-25Parts per billion17.30833340.22238Parts per million0.0271250.0511147Parts per billion0.4250000.823NaNParts per million0.4535420.92023NaN
1533741399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-25Parts per billion17.30833340.22238Parts per million0.0271250.0511147Parts per billion0.4250000.823NaNParts per million0.4708330.80019.0
1533841399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-25Parts per billion17.30833340.22238Parts per million0.0271250.0511147Parts per billion0.4458331.001.0Parts per million0.4535420.92023NaN
1533941399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-25Parts per billion17.30833340.22238Parts per million0.0271250.0511147Parts per billion0.4458331.001.0Parts per million0.4708330.80019.0
1534041399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-26Parts per billion14.57916735.2033Parts per million0.0420530.0601167Parts per billion0.6375001.201.0Parts per million0.4541670.80039.0
1534141399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-26Parts per billion14.57916735.2033Parts per million0.0420530.0601167Parts per billion0.6000001.12NaNParts per million0.4330000.8921NaN
1534241399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-26Parts per billion14.57916735.2033Parts per million0.0420530.0601167Parts per billion0.6375001.201.0Parts per million0.4330000.8921NaN
1534341399974530 N 17TH AVENUEArizonaMaricopaPhoenix2016-03-26Parts per billion14.57916735.2033Parts per million0.0420530.0601167Parts per billion0.6000001.12NaNParts per million0.4541670.80039.0